Tingfan Wu
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 1%
- Computer Science Applications top 0.5%
- Radiology, Nuclear Medicine and Imaging top 2%
- Experimental and Cognitive Psychology top 2%
- Co-authors
- Ruby C. WengChih‐Jen LinJavier R. MovellanJacob WhitehillPaul RuvoloMarian Stewart BartlettGwen LittlewortIan Fasel
- Topics
- Radiomics and Machine Learning in Medical Imaging (19 papers)Face and Expression Recognition (8 papers)Hepatocellular Carcinoma Treatment and Prognosis (8 papers)
- Cited by
- Computer Science ApplicationsComputer Vision and Pattern RecognitionArtificial Intelligence
- Partner nations
- ChinaUnited StatesGermany
In The Last Decade
Tingfan Wu
52 papers receiving 4.1k citations
Hit Papers
Peers
Comparison fields: 5 of 173
- Artificial Intelligence 1.4k
- Computer Vision and Pattern Recognition 1.1k
- Computer Science Applications 542
- Radiology, Nuclear Medicine and Imaging 518
- Experimental and Cognitive Psychology 488
Countries citing papers authored by Tingfan Wu
This map shows the geographic impact of Tingfan Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tingfan Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tingfan Wu more than expected).
Fields of papers citing papers by Tingfan Wu
This network shows the impact of papers produced by Tingfan Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tingfan Wu. The network helps show where Tingfan Wu may publish in the future.
Co-authorship network of co-authors of Tingfan Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Tingfan Wu. A scholar is included among the top collaborators of Tingfan Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tingfan Wu. Tingfan Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 11 | |
| 7 | 12 | |
| 8 | 4 | |
| 9 | 2 | |
| 10 | 9 | |
| 11 | 44 | |
| 12 | 37 | |
| 13 | 3 | |
| 14 | 27 | |
| 15 | 42 | |
| 16 | 18 | |
| 17 | 24 | |
| 18 | 49 | |
| 19 | Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertisebreakdown → | 662 |
| 20 | Probability Estimates for Multi-class Classification by Pairwise Couplingbreakdown → | 1124 |
About Tingfan Wu
Tingfan Wu is a scholar working on Hepatology, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 56 papers that have together received 4.3k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (19 papers), Face and Expression Recognition (8 papers) and Hepatocellular Carcinoma Treatment and Prognosis (8 papers). The work is most often cited by research in Computer Science Applications (542 citations), Computer Vision and Pattern Recognition (1.1k citations) and Artificial Intelligence (1.4k citations). Tingfan Wu has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Ruby C. Weng, Chih‐Jen Lin, Javier R. Movellan, Jacob Whitehill, Paul Ruvolo, Marian Stewart Bartlett, Gwen Littlewort, Ian Fasel, Mark G. Frank and Sylvain Bertrand. Their work appears in journals such as Medicine, Neurocomputing and IEEE Transactions on Robotics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.